Template-Type: ReDIF-Article 1.0
Author-Name: S. Geetha
Author-X-Name-First: S.
Author-X-Name-Last: Geetha
Author-Name: P. Deepalakshmi
Author-X-Name-First: P.
Author-X-Name-Last: Deepalakshmi
Title: Rapid retrieval of secured data from the sensor cloud using a relative record index and energy management of sensors
Abstract:
A massive amount of data is produced by sensors. These data finds a place in the cloud through a base station. Occasionally, the data collection process is disrupted as a result of the energy level of the sensor network due to voids. Void sensors do not propagate messages intended for the destination. We have addressed the issue of voids in sensors with the dynamic void removal algorithm. Retrieval of data from cloud is performed by relative record index associated with a security mechanism. Authenticated customers are given a secret key to rapidly retrieve data from the cloud. Meanwhile sensor networks require a secure mutual authentication scheme in an anxious network environment; our etiquette can handle all problems thrown up by the former schemes. Therefore, our protocol is more suited to an open and higher-security Sensor Network environment despite greater computation cost and energy.
Journal: Int. J. of Intelligent Enterprise
Pages: 3-14
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: wireless sensor network; WSN; sensor cloud; void sensors; dynamic void removal algorithm; DVRA; relative record index; RRI.
File-URL: http://www.inderscience.com/link.php?id=104641
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:3-14

Template-Type: ReDIF-Article 1.0
Author-Name: K. Muthamil Sudar
Author-X-Name-First: K. Muthamil
Author-X-Name-Last: Sudar
Author-Name: P. Deepalakshmi
Author-X-Name-First: P.
Author-X-Name-Last: Deepalakshmi
Title: Comparative study on IDS using machine learning approaches for software defined networks
Abstract:
Software defined networking (SDN) is an emerging network approach that separates the data plane from control plane and enables programmable features to efficiently handle the network configuration in order to improve network performance and monitoring. Since SDN contains the logically centralised controller which controls the entire network, the attacker mainly focuses on causing vulnerability towards the controller. Hence there is a need of powerful tool called intrusion detection system (IDS) to detect and prevent the network from various intrusions. Therefore, incorporation of IDS into SDN architecture is essential one. Nowadays, machine learning (ML) approaches can provide promising solution for the prediction of attacks with more accuracy and with low error rate. In this paper, we surveyed about some machine learning techniques such as naive Bayes, decision tree, random forest, multilayer perceptron algorithms for IDS and compare their performance in terms of attack prediction accuracy and error rate. Additionally, we also discussed about the background of SDN, security issues in SDN, overview of IDS types and various machine learning approaches with the knowledge of datasets.
Journal: Int. J. of Intelligent Enterprise
Pages: 15-27
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: intrusion detection system; IDS; machine learning; software defined networking; SDN; naive Bayes; decision trees; random forest; multilayer perceptron; datasets.
File-URL: http://www.inderscience.com/link.php?id=104642
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:15-27

Template-Type: ReDIF-Article 1.0
Author-Name: T. Ravikumar
Author-X-Name-First: T.
Author-X-Name-Last: Ravikumar
Title: Financial access indicators of financial inclusion: a comparative analysis of SAARC countries
Abstract:
Financial inclusion provides access to formal financial services at reasonable cost to the financially excluded people. Financial inclusion has been one of the most sought after topics in recent times for policy makers, researchers and academicians. Definition of financial inclusion varies from region to region. Financial inclusion is measured using different indicator. The important indicators of financial inclusion measurement include access indicators, usage indicators, quality indicators and financial education indicators. Most of the researchers use access indicators and usage indicators to measure financial inclusion. Access indicators comprise of demographic and geographic branch penetration, demographic and geographic ATM penetration and population per branch. This study focuses on comparative analysis of access indicators of financial inclusion in SAARC countries. The study is based on secondary data available in the central banks of SAARC nations, International Monetary Fund, World Bank and Asian Development Bank. The study has found and analysed about the countries which has performed well in each indicator of financial access.
Journal: Int. J. of Intelligent Enterprise
Pages: 28-36
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: financial access; financial inclusion; indicators; SAARC.
File-URL: http://www.inderscience.com/link.php?id=104643
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:28-36

Template-Type: ReDIF-Article 1.0
Author-Name: S. Packirisamy Balamurugan
Author-X-Name-First: S. Packirisamy
Author-X-Name-Last: Balamurugan
Author-Name: Gurusamy Arumugam
Author-X-Name-First: Gurusamy
Author-X-Name-Last: Arumugam
Title: A novel method for predicting kidney diseases using optimal artificial neural network in ultrasound images
Abstract:
The main aim of this research is to design and develop an efficient approach for predicting ultrasound kidney diseases using multiple stages. Nowadays, kidney disease prediction is one of the crucial procedures in surgical and treatment planning for ultrasound images. Therefore, in this paper, we propose a novel ultrasound kidney diseases prediction using the artificial neural network (ANN). To achieve the concept, we comprise the proposed system into four modules such as preprocessing, feature extraction, feature selection using OGOA and disease prediction using ANN. Initially, we eliminate the noise present in the input image using the optimal wavelet and bilateral filter. Then, a set of GLCM features are extracted from each input image and then we select the important features using oppositional grasshopper optimisation algorithm (OGOA). To classify the image as normal or abnormal, the proposed method utilises an artificial neural network (ANN). The performance of the proposed method is evaluated using accuracy, sensitivity, and specificity. The experimentation results show that the proposed system attains the maximum accuracy of 95.83% which is high compared to existing methods.
Journal: Int. J. of Intelligent Enterprise
Pages: 37-55
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: ultrasound image; neural network; multi-kernel k-means clustering; GLCM features; segmentation; classification; bilateral filter; oppositional grasshopper optimisation algorithm; OGOA.
File-URL: http://www.inderscience.com/link.php?id=104644
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:37-55

Template-Type: ReDIF-Article 1.0
Author-Name: Praveen Edward James
Author-X-Name-First: Praveen Edward
Author-X-Name-Last: James
Author-Name: Mun Hou Kit
Author-X-Name-First: Mun Hou
Author-X-Name-Last: Kit
Author-Name: Chockalingam Aravind Vaithilingam
Author-X-Name-First: Chockalingam Aravind
Author-X-Name-Last: Vaithilingam
Author-Name: Alan Tan Wee Chiat
Author-X-Name-First: Alan Tan Wee
Author-X-Name-Last: Chiat
Title: Recurrent neural network-based speech recognition using MATLAB
Abstract:
The purpose of this paper is to design an efficient recurrent neural network (RNN)-based speech recognition system using software with long short-term memory (LSTM). The design process involves speech acquisition, pre-processing, feature extraction, training and pattern recognition tasks for a spoken sentence recognition system using LSTM-RNN. There are five layers namely, an input layer, a fully connected layer, a hidden LSTM layer, SoftMax layer and a sequential output layer. A vocabulary of 80 words which constitute 20 sentences is used. The depth of the layer is chosen as 20, 42 and 60 and the accuracy of each system is determined. The results reveal that the maximum accuracy of 89% is achieved when the depth of the hidden layer is 42. Since the depth of the hidden layer is fixed for a task, increased performance can be achieved by increasing the number of hidden layers.
Journal: Int. J. of Intelligent Enterprise
Pages: 56-66
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: speech recognition; feature extraction; pre-processing; recurrent neural network; RNN; long short-term memory; LSTM; hidden layer; MATLAB.
File-URL: http://www.inderscience.com/link.php?id=104645
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:56-66

Template-Type: ReDIF-Article 1.0
Author-Name: R. Anand
Author-X-Name-First: R.
Author-X-Name-Last: Anand
Author-Name: T. Shanthi
Author-X-Name-First: T.
Author-X-Name-Last: Shanthi
Author-Name: R.S. Sabeenian
Author-X-Name-First: R.S.
Author-X-Name-Last: Sabeenian
Author-Name: S. Veni
Author-X-Name-First: S.
Author-X-Name-Last: Veni
Title: Real time noisy dataset implementation of optical character identification using CNN
Abstract:
Optical character recognition (OCR) is one of the major research problem in real time applications and it is used to recognise all the characters in an image. As English is a universal language, character recognition in English is a challenging task. Deep learning approach is one of the solution for the recognition of optical characters. Aim of this research work is to perform character recognition using convolutional neural network with LeNET architecture. Dataset used in this work is scanned passport dataset for generating all the characters and digits using tesseract. The dataset has training set of 60,795 and testing set of 7,767. Total samples used are 68,562 which is separated by 62 labels. Till now there is no research on predicting all 52 characters and ten digits. The algorithm used in this work is based on deep learning with appropriate some layer which shows significant improvement in accuracy and reduced the error rate. The developed model was experimented with test dataset for prediction and can produce 93.4% accuracy on training, and 86.5% accuracy on the test dataset.
Journal: Int. J. of Intelligent Enterprise
Pages: 67-80
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: convolutional neural networks; CNN; scanned passport; deep learning; classification; optical character recognition; OCR; discrete wavelet transform; DWT.
File-URL: http://www.inderscience.com/link.php?id=104646
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:67-80

Template-Type: ReDIF-Article 1.0
Author-Name: Rajeev Rajkumar
Author-X-Name-First: Rajeev
Author-X-Name-Last: Rajkumar
Author-Name: Sudipta Roy
Author-X-Name-First: Sudipta
Author-X-Name-Last: Roy
Author-Name: Khumanthem Manglem Singh
Author-X-Name-First: Khumanthem Manglem
Author-X-Name-Last: Singh
Title: S-transform-based efficient copy-move forgery detection technique in digital images
Abstract:
Copy-move forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. Due to the arrival of high-performance hardware and the compact use of image processing software, empowers creating image forgeries easy that are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stockwell transform (S-transform) which hybrids the advantages of both scale invariant feature transform (SIFT) and wavelet transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks are measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel fuzzy min-max neural network-based decision tree (FMMNN-DT) classifier is used to recognise the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensure the significance of the proposed.
Journal: Int. J. of Intelligent Enterprise
Pages: 107-121
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: copy-move forgery; CMF; Stockwell transform; S-transform; feature extraction; fuzzy min-max classifier; decision tree classifier.
File-URL: http://www.inderscience.com/link.php?id=104647
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:107-121

Template-Type: ReDIF-Article 1.0
Author-Name: G. Tamilpavai
Author-X-Name-First: G.
Author-X-Name-Last: Tamilpavai
Author-Name: R. Sripathy Padhma
Author-X-Name-First: R. Sripathy
Author-X-Name-Last: Padhma
Author-Name: C. Vishnuppriya
Author-X-Name-First: C.
Author-X-Name-Last: Vishnuppriya
Title: A computational perception of locating multiple longest common subsequence in DNA sequences
Abstract:
Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCS) is one of the indispensable issue to be unravelled viably in computational science. Discovering LCS is fundamental undertaking in deoxyribonucleic acid (DNA) arrangement investigation and other molecular biology. In this paper, new calculation for discovering LCS of two DNA successions and its area is proposed. The objective of this created framework is to discover the area and length of all subsequences which introduces in the two arrangements. To achieve this, DNA sequences are stored in an array and the comparison of DNA sequences are performed using matching algorithm. At the end of matching process, group of subsequence are obtained. Then the length and location of the matched subsequence are computed. After completing the matching process, longest common subsequence(s) is located. In this proposed work, maximally obtained length of LCS is 8. Finally, the computation time is calculated for locating LCS in DNA sequences. In addition to this, computation time is analysed by gradually increasing the length (in characters count) of DNA sequences from 100, 200, 300, 400 and 500. It concludes that computation time for locating LCS in various lengths of DNA sequences took few seconds difference only.
Journal: Int. J. of Intelligent Enterprise
Pages: 93-106
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: computational biology; deoxyribonucleic acid; DNA; longest common subsequence; LCS; matching algorithm; bioinformatics; molecular biology; NCBI; Matlab; intelligent computing.
File-URL: http://www.inderscience.com/link.php?id=104648
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:93-106

Template-Type: ReDIF-Article 1.0
Author-Name: G. Thenmozhi
Author-X-Name-First: G.
Author-X-Name-Last: Thenmozhi
Author-Name: J. Sreelatha
Author-X-Name-First: J.
Author-X-Name-Last: Sreelatha
Author-Name: S. Gobinaath
Author-X-Name-First: S.
Author-X-Name-Last: Gobinaath
Title: Analysis of double chambered single and cascaded microbial fuel cells: characterisation study based on the enrichment of fuel
Abstract:
Need for green energy, depletion of fossil fuels becomes the immediate requirement for building a clean and sustainable society. Among the various sustainable energy sources, microbial fuel cell is an emerging field with vast history as it converts the naturally available materials or bio-products into electricity with the help of microbes. Hence microbial fuel cell is an energy transducer. The experimental set-up is a double chambered microbial fuel cell with four single units among which two are separate and other two single units are cascaded into one. Cow dung and sheep worm kept in the anodic chamber are used both individually and also in combination. In order to make a complete characterisation study of MFC, additives like vermicompost, curd, etc., are added and they help to promote the growth of bacteria into it. This experimental setup, clearly defines the variation of output voltage and output power for the three different samples chosen with respect to time. Also the characterisation study is made based on the implementation of MFC set up with respect to the fuel enrichment and its performance is analysed.
Journal: Int. J. of Intelligent Enterprise
Pages: 81-92
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: microbial fuel cell; energy transducer; cascaded MFC; double chambered microbial fuel cell; cow dung; sheep worm; vermicompost; fuel enrichment; clean energy; characterisation study.
File-URL: http://www.inderscience.com/link.php?id=104649
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:81-92

Template-Type: ReDIF-Article 1.0
Author-Name: Jagan Nath
Author-X-Name-First: Jagan
Author-X-Name-Last: Nath
Author-Name: Rajesh Kumar Aggarwal
Author-X-Name-First: Rajesh Kumar
Author-X-Name-Last: Aggarwal
Author-Name: Yudhvir Singh
Author-X-Name-First: Yudhvir
Author-X-Name-Last: Singh
Title: Enhanced media independent handover for vertical handover decision in MANET
Abstract:
In heterogeneous mobile ad hoc network (MANET), seamless connectivity of a mobile node is the important challenge. Due to the mobility of the node, it may loss seamless connection. So, vertical handover techniques were presented to solve this issue. However, communication might get cancelled when handover takes place. This results in call drop and some other issues. So as to overcome these issues, an enhanced media independent handover/IEEE802.21 (EMIH) for vertical handover decision is presented in this paper. In this standard, adaptive neuro-fuzzy inference system (ANFIS) is included to select optimal network for vertical handover. Simulation results show that performance of the proposed approach outperforms that of the existing approach in terms of handover probability, drop, etc.
Journal: Int. J. of Intelligent Enterprise
Pages: 122-136
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: mobile ad hoc network; MANET; vertical handover; optimal network; adaptive neuro-fuzzy inference system; ANFIS; received signal strength; RSS; average bit rate; ABR; handover probability.
File-URL: http://www.inderscience.com/link.php?id=104650
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:122-136

Template-Type: ReDIF-Article 1.0
Author-Name: A. Varadaraj
Author-X-Name-First: A.
Author-X-Name-Last: Varadaraj
Author-Name: S. Ananth
Author-X-Name-First: S.
Author-X-Name-Last: Ananth
Title: The effect of lean on job satisfaction
Abstract:
Lean principles and lean management are increasingly implemented in various sectors of organisations. Lean has shown visible effects in enhancing productivity, reducing wastage of time and materials while still maintaining customer satisfaction as well as employee satisfaction. Lean philosophy is about people understanding their motives and aspirations. Most of the literature works on lean say that the key driver for lean implementation is employee involvement and satisfaction with the process. Hence lean always focuses on employee motivation and their work performance. This thesis is proposed to study on the impact of lean on job satisfaction in organisations. The research data will be collected using the survey tool by distributing questionnaires to organisations that implement lean principles. The respondents who belong to the group that handles everyday work process and services will be selected to participate in the survey. The research survey data will be analysed using Microsoft Excel along with statistical tools like Correlation in order to have an in depth understanding on the findings of the research proposed through several hypothesis.
Journal: Int. J. of Intelligent Enterprise
Pages: 137-154
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: enhancing productivity; reducing wastage of time; limitation of materials; employee motivation.
File-URL: http://www.inderscience.com/link.php?id=104651
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:137-154

Template-Type: ReDIF-Article 1.0
Author-Name: S.L. Sanjith
Author-X-Name-First: S.L.
Author-X-Name-Last: Sanjith
Author-Name: E. George Dharma Prakash Raj
Author-X-Name-First: E. George Dharma Prakash
Author-X-Name-Last: Raj
Title: Reinforcement-based heterogeneous ensemble for anomaly detection in streaming environment
Abstract:
Intrusion detection in networks is a challenging process, mainly due to huge amount of data and the imbalanced nature of the data. Further, the ever-changing transmission patterns introduce concept drift, which also exhibits a huge challenge. This work presents a heterogeneous ensemble based prediction model to detect anomalies in the network environment. The major goal of the proposed model is to provide faster, more efficient real-time predictions and to enhance the reliability of the model by providing an iterative mechanism to handle concept drifts. The ensemble is created using three varied base learners and the results are aggregated using a voting combiner to provide results. Decision tree, random forest, and gradient boosting trees are used as the base learners. The varied nature of the learners enables effective performances in models. Further reinforcement and an iterative training component is introduced into the model to handle concept drift. Experiments were performed on benchmark intrusion detection data and the results indicate the high performing nature of the model. Comparisons were performed with recent state-of-the-art models in literature and they indicate improved performances of the proposed model, indicating the high performing nature of the proposed ensemble model.
Journal: Int. J. of Intelligent Enterprise
Pages: 155-165
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: ensemble model; decision tree; random forest; gradient boosting trees; voting; anomaly detection.
File-URL: http://www.inderscience.com/link.php?id=104652
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:155-165

Template-Type: ReDIF-Article 1.0
Author-Name: B.R. Aravind
Author-X-Name-First: B.R.
Author-X-Name-Last: Aravind
Author-Name: V. Rajasekaran
Author-X-Name-First: V.
Author-X-Name-Last: Rajasekaran
Title: Using technological modality to learn vocabulary incidentally and intentionally for effective communication
Abstract:
Vocabulary is the flesh of a language, which is an indispensable constituent for a language. This research highlights the role of language learners' acquisition in incidental and intentional vocabulary by using technological modality. Effective usage of vocabulary in communication and comprehension is crucial and demanding as well. English being the diplomatic language, and which is witnessed as a parameter for graduates, particularly in job acquisition. There are numerous teaching methods were followed for effective learning. In order to benefit, English as a second language (ESL) learners and English as a foreign language (EFL) learners, task-based learning (TBL) approach is observed to be an effective learning method. This paper devices to use technology, entertainment, and design (TED) talk video with subtitles in the syllabus of TBL learning for effective learning of incidental and intentional vocabulary in language and succeeded by analysing the response from the students. The study reveals the significant development and interest in learning a new word by using the authentic instructional TED talk videos for vocabulary learning and vocabulary acquisition.
Journal: Int. J. of Intelligent Enterprise
Pages: 166-175
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: English as a second language; ESL; English as a foreign language; EFL; task-based learning; TBL; vocabulary; English and communication.
File-URL: http://www.inderscience.com/link.php?id=104653
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:166-175

Template-Type: ReDIF-Article 1.0
Author-Name: Suja Chandrasekharan Nair
Author-X-Name-First: Suja Chandrasekharan
Author-X-Name-Last: Nair
Author-Name: M. Sudheep Elayidom
Author-X-Name-First: M. Sudheep
Author-X-Name-Last: Elayidom
Author-Name: Sasi Gopalan
Author-X-Name-First: Sasi
Author-X-Name-Last: Gopalan
Title: Call detail record-based traffic density analysis using global K-means clustering
Abstract:
With the expanding number of vehicles on the road is creating substantial traffic that is hard to control and maintain safety, particularly in extensive urban areas. To estimate the traffic density several works were carried out in the past. However, they are inappropriate and expensive due to the dynamics of traffic flow. Here we intend to use CDR to distinguish the traffic density location and to track the location of the mobile user. In our proposed method to discover the density scope of the traffic, we are using two algorithms called k-means clustering and the k nearest neighbour classification algorithms. The proposed technique will be tested among five different locations during the weekdays and the weekends, which show the noteworthiness of the proposed algorithm and show that our technique has high accuracy.
Journal: Int. J. of Intelligent Enterprise
Pages: 176-187
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: traffic density; call detail records; CDR; data pre-processing; global K-means clustering algorithm; K-nearest neighbour classification; cell-tower ID; behavioral patterns; disposition; monitoring; predictable.
File-URL: http://www.inderscience.com/link.php?id=104654
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:176-187

Template-Type: ReDIF-Article 1.0
Author-Name: Periasamy Mathivanan
Author-X-Name-First: Periasamy
Author-X-Name-Last: Mathivanan
Author-Name: Ramaiah Kasilingam
Author-X-Name-First: Ramaiah
Author-X-Name-Last: Kasilingam
Title: Regulations on sustainability reporting as a global force in shaping business enterprises: evidence from India
Abstract:
McKinsey in 2010 identified the larger role of the 'state' as a business regulator as one of the five global forces that shape business enterprises. In the recent past, this was very evident in India when both the government and the stock market regulator introduced changes in business responsibility reporting of Indian enterprises. Intelligent enterprises adapt swiftly to changing regulatory mechanisms be it voluntary or mandatory. In this paper, we discuss how Indian enterprises respond to sustainability reporting requirements both in the voluntary and mandatory regimes. Among the variables identified for our study company's age, industry type, market capitalisation and listing status of the company including index type influences global sustainability reporting practices in India.
Journal: Int. J. of Intelligent Enterprise
Pages: 188-202
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: GRI; sustainability reporting in India; mandatory BRR; sustainability index; sustainable companies; SRTs; India.
File-URL: http://www.inderscience.com/link.php?id=104655
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:188-202

Template-Type: ReDIF-Article 1.0
Author-Name: E. Laxmi Lydia
Author-X-Name-First: E. Laxmi
Author-X-Name-Last: Lydia
Author-Name: Sivakoti Satyanarayan
Author-X-Name-First: Sivakoti
Author-X-Name-Last: Satyanarayan
Author-Name: K. Vijaya Kumar
Author-X-Name-First: K. Vijaya
Author-X-Name-Last: Kumar
Author-Name: Dasari Ramya
Author-X-Name-First: Dasari
Author-X-Name-Last: Ramya
Title: Indexing documents with reliable indexing techniques using Apache Lucene in Hadoop
Abstract:
Mostly 85% of the data is presented in the form of text, which is the human-readable format. Present educational, business, medical organisations, etc. making use of big data analytics for storage of data and processing that stored data by using information retrieval. Often time's text documents have been transferred from one system to another system without any restrictions like, structured, unstructured and semi-structured data. Systems are well performed with high speed and less complexity only when it has all the data arranged in an orderly way. This paper describes how documents of text data are being Indexed using Apache Lucene with approaches in Hadoop. Most of the applications that deal with huge data over the internet are completely lacking. Use of effective analysis and techniques allow users in resulting high-performance and a challenging option in leading big data analytics.
Journal: Int. J. of Intelligent Enterprise
Pages: 203-214
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: Apache Lucene; indexing; big data; indexing techniques.
File-URL: http://www.inderscience.com/link.php?id=104656
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:203-214

Template-Type: ReDIF-Article 1.0
Author-Name: Chemmalar Selvi Govardanan
Author-X-Name-First: Chemmalar Selvi
Author-X-Name-Last: Govardanan
Author-Name: Lakshmi Priya Gopalsamy Gnanapandithan
Author-X-Name-First: Lakshmi Priya Gopalsamy
Author-X-Name-Last: Gnanapandithan
Title: SMSS: does social, mobile, spatial and sensor data have high impact on big data analytics
Abstract:
Big data refers to the huge torrent of large-scale datasets that are being generated at an exponential growth. Since we live in this digital world, the era of big data has emerged in part and parcel of our lives. The emergence of big data has reached in almost several domains like healthcare industry, telecom industry, molecular biology, biochemistry, physics, astronomy, computer science, business and others. In this paper, we have termed the types of big data by the form SMSS data which is simply meaning social, mobile, spatial and sensor data. This paper aims to provide the importance of big data analytics brought over the different types of big data extracted from heterogeneous data sources. To achieve this objective, we have made an intensive study of several literatures and considered a variety of big data applications which are being discussed to showcase its value. Also, a generic framework is proposed that can be applicable to any kind of big data types extracted from such a diverse heterogeneous data sources. Finally, a few open source tools that can be used for processing the big data are presented.
Journal: Int. J. of Intelligent Enterprise
Pages: 215-233
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: big data types; social data; spatial data; sensor data; mobile data.
File-URL: http://www.inderscience.com/link.php?id=104657
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:215-233

Template-Type: ReDIF-Article 1.0
Author-Name: S. Ramkumar
Author-X-Name-First: S.
Author-X-Name-Last: Ramkumar
Author-Name: K. Sathesh Kumar
Author-X-Name-First: K. Sathesh
Author-X-Name-Last: Kumar
Author-Name: K. Maheswari
Author-X-Name-First: K.
Author-X-Name-Last: Maheswari
Author-Name: P. Packia Amutha Priya
Author-X-Name-First: P. Packia Amutha
Author-X-Name-Last: Priya
Author-Name: G. Emayavaramban
Author-X-Name-First: G.
Author-X-Name-Last: Emayavaramban
Author-Name: J. Macklin Abraham Navamani
Author-X-Name-First: J. Macklin Abraham
Author-X-Name-Last: Navamani
Title: Offline study for implementing human computer interface for elderly paralysed patients using electrooculography and neural networks
Abstract:
Earlier day's people with disability face lot of difficulty in communication due to neuromuscular attack. They are unable to share ideas and thoughts with others so they need some assist to overcome this condition. To overcome the condition, in this paper, we discussed the capabilities of designing electrooculogram (EOG)-based human computer interface (HCI) by ten subjects using power spectral density techniques and neural network. In this study, we compare the right hander performance with left hander performance. Outcomes of the study concluded that lefthander performance was marginally appreciated compared to right hander performance in terms of classification accuracy with an average accuracy of 93.38% for all left hand subjects and 91.38% for all the right subjects using probabilistic neural network (PNN) and also we analysed that during the training left handers were interestingly participated and also they can able to perform the following eleven tasks easily compared with right handers.
Journal: Int. J. of Intelligent Enterprise
Pages: 306-321
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: electrooculography; EOG; periodogram; human computer interface; HCI; probabilistic neural network; PNN.
File-URL: http://www.inderscience.com/link.php?id=104658
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:306-321

Template-Type: ReDIF-Article 1.0
Author-Name: Debasis Mukherjee
Author-X-Name-First: Debasis
Author-X-Name-Last: Mukherjee
Author-Name: B.V. Ramana Reddy
Author-X-Name-First: B.V. Ramana
Author-X-Name-Last: Reddy
Title: Design of cost effective transistor by software simulation for profitable production
Abstract:
Reduction of process cost is the key factor for profitability in any industry. Semiconductor industry is also not an exception of this rule. In this paper, a novel transistor structure has been proposed with reduced process cost and almost same functionality compared to conventional MOSFET transistor. Details fabrication steps of the novel transistor have been proposed. Working of the proposed structure resembles conventional MOSFET, but structure wise there are many differences. Necessity of source extension and drain extension has been uninvolved, resulting less fabrication cost and higher concentration of transistors in same chip area. Another improvement is removal of gate spacer, resulting cutting down of process cost. Both the conventional MOSFET and the proposed one have been simulated by Sentaurus TCAD toolkit for 7 nm technology generation. The performance of the proposed transistor has been found satisfactory compared to the conventional MOSFET as per the guidance given in International Technology Roadmap for Semiconductors or ITRS (2013) version.
Journal: Int. J. of Intelligent Enterprise
Pages: 291-305
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: 7 nm; cost; CMOS; device level; fabrication; ITRS; MOSFET; process cost; production; profitability; TCAD; VLSI.
File-URL: http://www.inderscience.com/link.php?id=104659
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:291-305

Template-Type: ReDIF-Article 1.0
Author-Name: Nikhila T. Bhuvan
Author-X-Name-First: Nikhila T.
Author-X-Name-Last: Bhuvan
Author-Name: M. Sudheep Elayidom
Author-X-Name-First: M. Sudheep
Author-X-Name-Last: Elayidom
Title: A supervised multimodal search re-ranking technique using visual semantics
Abstract:
The multimedia content in a webpage is usually given least importance in webpage ranking. A better user satisfaction could be achieved if the web pages are ranked based on multiple modalities rather than just depending on the textual content. A better ranking of the web pages is proposed using natural language descriptions of images along with the textual content in a webpage is being proposed. The inter-modal correspondences between text and visual data are learned using the convolutional neural network assisted by the datasets of images and their sentence descriptors. The model is based on convolutional neural networks over images to generate the image descriptor and Dandelion API for their similarity measure with the query. The image description is algorithmically generated rather depending on the image annotations present. Finally, it has been proven that the re-ranked web pages using the generated descriptions significantly outperform the state of art retrieval models.
Journal: Int. J. of Intelligent Enterprise
Pages: 279-290
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: automatic image annotation; convolutional neural networks; image descriptor; multimodality search; search re ranking; semantic similarity; supervised re ranking; visual semantics.
File-URL: http://www.inderscience.com/link.php?id=104660
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:279-290

Template-Type: ReDIF-Article 1.0
Author-Name: Vaibhav J. Hase
Author-X-Name-First: Vaibhav J.
Author-X-Name-Last: Hase
Author-Name: Yogesh J. Bhalerao
Author-X-Name-First: Yogesh J.
Author-X-Name-Last: Bhalerao
Author-Name: Saurabh Verma
Author-X-Name-First: Saurabh
Author-X-Name-Last: Verma
Author-Name: G.J. Vikhe Patil
Author-X-Name-First: G.J. Vikhe
Author-X-Name-Last: Patil
Title: Intelligent systems for volumetric feature recognition from CAD mesh models
Abstract:
This paper presents an intelligent technique to recognise the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet-based, vertex based, rule-based, and artificial neural network (ANN)-based techniques. Comparing with existing state-of-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. ANN-based intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favourably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features.
Journal: Int. J. of Intelligent Enterprise
Pages: 267-278
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: CAD mesh model; CMM; hybrid mesh segmentation; volumetric feature recognition.
File-URL: http://www.inderscience.com/link.php?id=104661
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:267-278

Template-Type: ReDIF-Article 1.0
Author-Name: T. Venkata Satya Vivek
Author-X-Name-First: T. Venkata Satya
Author-X-Name-Last: Vivek
Author-Name: V.N. Rajavarman
Author-X-Name-First: V.N.
Author-X-Name-Last: Rajavarman
Author-Name: Srinivasa Rao Madala
Author-X-Name-First: Srinivasa Rao
Author-X-Name-Last: Madala
Title: Advanced graphical-based security approach to handle hard AI problems based on visual security
Abstract:
Security is the main aspect to explore human data from different web oriented applications present in artificial intelligence (AI). It is very difficult to use different web applications without security to access data in various places. So that various types of security related approaches were introduced to use services in securely in outside environment, but they have some limitations to protect data from outside attackers (hackers). So that in this paper, we propose and introduce a novel and advanced security model to provide security from outside attackers in AI related web oriented applications. In this approach, we follow the basic features related to Captcha as a graphical password to enable security services in our proposed approach. Using Captcha graphical passwords in our approach, we describe pushing attacks, pass-on attacks and guessing attacks in web applications with random selection of Captcha passwords to use web services. Our experimental results show efficient security relations when compare to existing security approaches in terms of Captcha generation, time and other parameters present in web security applications.
Journal: Int. J. of Intelligent Enterprise
Pages: 250-266
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: Captcha; graphical password; directory-based push attacks; security attacks; visual cryptography; dictionary attacks.
File-URL: http://www.inderscience.com/link.php?id=104662
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:250-266

Template-Type: ReDIF-Article 1.0
Author-Name: Amir Ahmad Dar
Author-X-Name-First: Amir Ahmad
Author-X-Name-Last: Dar
Author-Name: N. Anuradha
Author-X-Name-First: N.
Author-X-Name-Last: Anuradha
Title: Studies on European call option of binomial option pricing model using Taguchi's L27 orthogonal array
Abstract:
There are several parameters affecting the European call option value such as strike price <i>K</i>, the price of an underlying asset <i>S</i><SUB align="right"><SMALL>0</SMALL></SUB>, volatility <i>σ</i>, time period <i>t</i> and interest rate <i>r</i>. In this paper, the binomial option pricing model is utilised to assess the estimation of a European call option. To explore the effects of input factors, Taguchi method of orthogonal L27 design experiment is carried out using an orthogonal array, analysis of variance (ANOVA), and analysis of mean (ANOM) were used. The purpose of this paper to find the best optimal combination by varying the parameters at constant interest rate <i>r</i> and the effects of parameters are discussed. The ANOM distinguishes which parameter influences higher on European call option value and furthermore, it demonstrates the best combination where the European call option will get the greatest value. The ANOVA estimates the percentage contribution of every parameter on European call option and the analysis is carried out using MINITAB software.
Journal: Int. J. of Intelligent Enterprise
Pages: 234-249
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: binomial model; Taguchi's method; analysis of mean; ANOM; analysis of variance; ANOVA; option.
File-URL: http://www.inderscience.com/link.php?id=104663
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:234-249

Template-Type: ReDIF-Article 1.0
Author-Name: V.R. Dheera
Author-X-Name-First: V.R.
Author-X-Name-Last: Dheera
Author-Name: Jayasree Krishnan
Author-X-Name-First: Jayasree
Author-X-Name-Last: Krishnan
Title: Influence of human resource management practices on the organisational commitment with specific reference to selected hotels in Chennai
Abstract:
This study investigates the influence of human resource management (HRM) practices on the organisational commitment in hospitality industry. The study hypothesises that HRM practices (employee motivation, rewards and awards, grievance handling, employee engagement, performance appraisal and training and development) will be positively related to commitment to organisation and career. The study was conducted with randomly selected employees (300 numbers) of leading hotels in Chennai, Tamilnadu. The statistical results of the data collected from the employees of hotels reveal that majority of the six HRM practices have direct positive and significant relationships with commitment to organisation and career. The employees of the hotels felt that 'grievance handling' function of HRM practices has to be given more importance and 'performance appraisal system' has to be more effective in a manner to motivate employees to perform better.
Journal: Int. J. of Intelligent Enterprise
Pages: 322-337
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: HRM practices; motivation; rewards and awards; grievance handling; employee engagement; performance appraisal; training and development; commitment to organisation and career; hotel industry.
File-URL: http://www.inderscience.com/link.php?id=104664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:322-337

Template-Type: ReDIF-Article 1.0
Author-Name: V.T. Venkateswarlu
Author-X-Name-First: V.T.
Author-X-Name-Last: Venkateswarlu
Author-Name: P.V. Naganjaneyulu
Author-X-Name-First: P.V.
Author-X-Name-Last: Naganjaneyulu
Author-Name: D.N. Rao
Author-X-Name-First: D.N.
Author-X-Name-Last: Rao
Title: Rendezvous agents-based routing protocol for delay sensitive data transmission over wireless sensor networks with mobile sink
Abstract:
The data collected by the sensor nodes will be transferred to sink in traditional wireless sensor networks. Due to the constrained energy reserves of the sensors, a transmission route should operate with minimal energy that tends to longevities network survival. A novel routing is portrayed that partitions the network area into regions and establishes rendezvous agents for the mobile sink at all of these regions and defines a method to order the areas which is followed by the mobile sink to visit the regions. The process of ordering the regions is furbished under many quality of service objectives portrayed in this manuscript. The performance of the proposed model assessed through simulation study and the same is compared with other contemporary model having similar objectives. The energy consumption efficiency under optimal packet delivery with minimal latency is the objective considered for the performance analysis.
Journal: Int. J. of Intelligent Enterprise
Pages: 338-355
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: LEACH protocol; PASCCC; line-based data dissemination; rendezvous points; smoke/CO system.
File-URL: http://www.inderscience.com/link.php?id=104665
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:338-355

Template-Type: ReDIF-Article 1.0
Author-Name: Ranjan Kumar Dash
Author-X-Name-First: Ranjan Kumar
Author-X-Name-Last: Dash
Title: Design of data scoring model for big data
Abstract:
The huge volume and variety of data stored in big data provide more accurate predictive platform for the users. However, the decision-making process becomes a tedious task due to requirement of much computational time and memory to access them. Thus, a solution to the said problem is data scoring that provides the selection of only those variables or features that impact the decision-making process to a greater extend. To cater the need of an efficient data scoring model, the work carried out in this paper proposes a new data scoring model for big data. The proposed model uses adaptive LASSO as the statistical method. The steps involved in the design of the proposed model are outlined with proper explanation. The model is trained and tested by k-fold cross validation technique. The performance of the model is measured using ROC curve. The model is simulated using R and is applied on three distinct datasets. To make a comparison with LASSO, LASSO is also applied on these datasets. The simulated results reveal that the adaptive LASSO performs better than LASSO for large-sized datasets.
Journal: Int. J. of Intelligent Enterprise
Pages: 356-371
Issue: 1/2/3
Volume: 7
Year: 2020
Keywords: big data; regression analysis; data scoring; receiver operating characteristic curves; discriminant analysis; decision tree; support vector machine; random forest; intelligent system.
File-URL: http://www.inderscience.com/link.php?id=104666
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:356-371

Template-Type: ReDIF-Article 1.0
Author-Name: Petrus Setya Murdapa
Author-X-Name-First: Petrus Setya
Author-X-Name-Last: Murdapa
Author-Name: I. Nyoman Pujawan
Author-X-Name-First: I. Nyoman
Author-X-Name-Last: Pujawan
Author-Name: Putu Dana Karningsih
Author-X-Name-First: Putu Dana
Author-X-Name-Last: Karningsih
Author-Name: Arman Hakim Nasution
Author-X-Name-First: Arman Hakim
Author-X-Name-Last: Nasution
Title: Incorporating carbon emissions in queuing models to determine lot sizes and inventory buffers in a supply chain
Abstract:
In this paper, we present a supply chain model that considers both inventory-related costs and emissions. We used the queueing-based performance model wherein emissions in three stages of supply chain activities are captured. The model was solved by the decomposition approach. For model validation, we have used a discrete event simulation. The computation results show that the two results, i.e., the decomposition approach and the simulation, are very close, indicating the accuracy the approach that we used. Experiments were conducted to test the applicability of the model. The numerical examples show that the change in parameter values is not always responded the same way by the total inventory-related costs and the emission costs, indicating the importance of including these two response variables in the model.
Journal: Int. J. of Intelligent Enterprise
Pages: 373-390
Issue: 4
Volume: 7
Year: 2020
Keywords: carbon emission; inventory buffering; lot sizing; queuing; performance model; supply chain.
File-URL: http://www.inderscience.com/link.php?id=110757
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:373-390

Template-Type: ReDIF-Article 1.0
Author-Name: Samira Boussema
Author-X-Name-First: Samira
Author-X-Name-Last: Boussema
Title: Entrepreneurial passion facing its ecosystem's obstacles: the case of Tunisia
Abstract:
Entrepreneurship is one of the most appropriate remedies to the various economic crises. It is presented as a complex process that faces several barriers, thereby inhibiting a project's implementation phase. In fact, after a careful review of the literature, we noticed that empirical research on reasons behind unimplemented entrepreneurial projects are very rare, suggesting a failure in modelling the process in general and the pre-start phase in particular. In this paper, we try then to identify the main constraints to entrepreneurial passion in Tunisia by studying a representative sample of project promoters who have been unable to carry out their business projects. Using structural equation methods, we found that these promoters face barriers like a lack in entrepreneurial training and services provided by supporting organisations.
Journal: Int. J. of Intelligent Enterprise
Pages: 391-404
Issue: 4
Volume: 7
Year: 2020
Keywords: entrepreneurial passion; unimplementation of projects; structural modelling.
File-URL: http://www.inderscience.com/link.php?id=110760
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:391-404

Template-Type: ReDIF-Article 1.0
Author-Name: Ram Naresh Roy
Author-X-Name-First: Ram Naresh
Author-X-Name-Last: Roy
Title: Implementing just-in-time-based supply chain for the bulk items in an integrated steel plant
Abstract:
Due to increasing global competition, organisations are continuously improving their operational practices and cost efficiency to get a competitive edge. This paper involves a case study in ABC Steel plant (anonymised) dealing with a huge amount of bulk-materials handling and transportation, and proposes a JIT-based model of handling and transporting which may lead to potential cost savings. The paper discusses the important requirements of JIT procurement and transportation through a literature review. The existing system of ABC Steel plant has been studied and modelled as a pull-system, and an MRP model has been used to calculate the amount of various raw materials needed for making the hot-iron or steel. The total costs of transporting bulk materials from various sources for steelmaking in the 'existing system (model 1)' and the 'proposed JIT system (model 2)' have been calculated. The differences between the two indicated the potential savings for different levels of safety-stock and different levels of JIT implementation.
Journal: Int. J. of Intelligent Enterprise
Pages: 405-422
Issue: 4
Volume: 7
Year: 2020
Keywords: JIT supply chain; logistics; bulk materials; integrated steel plant; MRP model; cost-savings and productivity; lean procurement.
File-URL: http://www.inderscience.com/link.php?id=110762
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:405-422

Template-Type: ReDIF-Article 1.0
Author-Name: Iram Naim
Author-X-Name-First: Iram
Author-X-Name-Last: Naim
Author-Name: Tripti Mahara
Author-X-Name-First: Tripti
Author-X-Name-Last: Mahara
Title: Framework to identify a set of univariate time series forecasting techniques to aid in business decision making
Abstract:
Forecasting is generally involved in business activities to anticipate or predict the future. With availability of numerous techniques and models, forecasters regularly face a genuine issue to identify suitable technique for different time series available in an organisation. Most of the time, it is not possible to find one technique that can be used for all-time series as the selection is dependent upon the characteristics of a time series. Hence, the research proposes a selection tree to aid in decision making based upon availability of type of dataset and time series characteristics. The framework is validated using four real case studies. This study also presents advancement to existing forecasting method selection tree by exploring a new dimension of complex seasonal pattern for long time series.
Journal: Int. J. of Intelligent Enterprise
Pages: 423-443
Issue: 4
Volume: 7
Year: 2020
Keywords: univariate time series; time series pattern; model selection; trend analysis; seasonal data; complex seasonality; long time series.
File-URL: http://www.inderscience.com/link.php?id=110764
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:423-443

Template-Type: ReDIF-Article 1.0
Author-Name: Mandy Mok Kim Man
Author-X-Name-First: Mandy Mok Kim
Author-X-Name-Last: Man
Author-Name: Lo May Chiun
Author-X-Name-First: Lo May
Author-X-Name-Last: Chiun
Title: Innovativeness, environment and performance of small and medium-sized enterprises in manufacturing sector in Malaysia
Abstract:
This study examines innovativeness and environmental factors that can influence the performance of small and medium-sized enterprises (SMEs) in the Malaysian manufacturing sector. Previous research have focused mainly on technological innovation rather than innovativeness in administration process and innovation culture. The present study examines the fundamental nature of innovativeness and relates these various elements of innovativeness to SMEs performance. The present study includes the role of the government. Firstly, in the Malaysian context, the government interferes in the market through new business policies, increasing or decreasing the interest rate, controlling money supply and implementing competition policy law. Secondly, the role of government has been neglected in most previous research in measuring the environment influences on business in spite of their importance in determining the environment indicators, such as, environmental uncertainty and intensity of competition. The present study shows that the innovativeness and environmental factors have significant impact on SMEs performance.
Journal: Int. J. of Intelligent Enterprise
Pages: 444-460
Issue: 4
Volume: 7
Year: 2020
Keywords: innovativeness; small and medium-sized enterprises; SMEs; environment; performance; technology; entrepreneurship.
File-URL: http://www.inderscience.com/link.php?id=110771
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:444-460

Template-Type: ReDIF-Article 1.0
Author-Name: J. Reddy
Author-X-Name-First: J.
Author-X-Name-Last: Reddy
Author-Name: A. Telukdarie
Author-X-Name-First: A.
Author-X-Name-Last: Telukdarie
Title: Modelling for system fluctuations advancing buffer management delivering on the theory of constraints
Abstract:
The aircraft component manufacturing industry can be considered a high value manufacturing value chain, due to the nature of the delivery space. South Africa currently supplies various components into the international aerospace industry. A key strategic operational toolset is the theory of constraints (TOC) manufacturing methodology. This research propositions a review and optimisation of the current mode of operation (MOO) specific to work in progress (WIP) management. A simulation-based approach is adopted to test the scenarios for potential work schedule optimisation, including incorporation of the TOC principals. The results are insightful, specific to throughput and cost management. The research and simulation serves as a significant opportunity for the company to integrate and optimise leading towards Industry 4.0 delivery.
Journal: Int. J. of Intelligent Enterprise
Pages: 461-480
Issue: 4
Volume: 7
Year: 2020
Keywords: manufacturing systems; Industry 4.0; theory of constraints; TOC; multimethod simulation modelling; production optimisation.
File-URL: http://www.inderscience.com/link.php?id=110781
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:461-480

Template-Type: ReDIF-Article 1.0
Author-Name: Taanika Arora
Author-X-Name-First: Taanika
Author-X-Name-Last: Arora
Author-Name: Arvind Kumar
Author-X-Name-First: Arvind
Author-X-Name-Last: Kumar
Author-Name: Bhawna Agarwal
Author-X-Name-First: Bhawna
Author-X-Name-Last: Agarwal
Title: Impact of social media advertising on millennials buying behaviour
Abstract:
The phenomenal growth of social media sites, has enticed the companies to target their consumers by advertising through most used mediums, hence it becomes crucial for the advertisers to carefully design the ads thereafter also check its effectiveness. The purpose of this paper is to propose a conceptual model which determines the impact of various advertising content factors such as informativeness, entertainment, credibility, interactivity and privacy concerns on attitude of Indian millennials towards social media advertising. Using non-probability sampling, the data was collected using the online questionnaire through Google Forms from a total of 470 social media users. The adapted scales have been validated through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), after which path analysis has been applied using SPSS AMOS 22.0 for testing the various formulated hypothesis. The results indicated significant relationships which can be useful in understanding the attitude and behavioural responses of Indian millennials towards social media advertising. The study can be useful to the marketers, advertisers and brand managers in designing advertisements on social media sites by embedding certain essential features which can positively shape up the attitudes and further develop behavioural responses.
Journal: Int. J. of Intelligent Enterprise
Pages: 481-500
Issue: 4
Volume: 7
Year: 2020
Keywords: social media; millennials; informativeness; entertainment; credibility; interactivity; privacy concerns; social media advertising; attitude; behavioural responses; buying behaviour; Indians.
File-URL: http://www.inderscience.com/link.php?id=110795
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:ids:ijient:v:7:y:2020:i:4:p:481-500